Depth Analysis of Planar Array for 3D Electrical Impedance Tomography

Chen, Bin; Soleimani, Masoud

doi:10.1109/jsen.2019.2929625

Cited by 12 publications

(3 citation statements)

References 21 publications

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“…Different kernel offsets emphasize different features within the algae cells (Fig. 8), and it would be natural to imagine combining these types of measurements with algorithms from electrical impedance tomography [12] to generate a 3-D reconstruction of the sample permittivity.…”

Section: Super-resolution Impedance Imagingmentioning

confidence: 99%

Super-Resolution Electrochemical Impedance Imaging with a 100 × 100 CMOS Sensor Array

Arcadia

Rosenstein

2021

Preprint

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This paper presents a 100 × 100 super-resolution integrated sensor array for microscale electrochemical impedance spectroscopy (EIS) imaging. The system is implemented in 180 nm CMOS with 10μm × 10μm pixels. Rather than treating each electrode independently, the sensor is designed to measure the mutual capacitance between programmable sets of pixels. Multiple spatially-resolved measurements can then be computationally combined to produce super-resolution impedance images. Experimental measurements of sub-cellular permittivity distributions within single algae cells demonstrate the potential of this new approach.

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Section: Super-resolution Impedance Imagingmentioning

confidence: 99%

Super-Resolution Electrochemical Impedance Imaging with a 100 × 100 CMOS Sensor Array

Arcadia

Rosenstein

2021

Preprint

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“…The process of reconstructing conductivity distribution from boundary measurement is defined as the inverse problem of EIT. 32 For small change of conductivity distribution Δσ, the boundary voltage variation ΔU can be simplified and formulated as…”

Section: Modeling Of Eitmentioning

confidence: 99%

“…It should be remarked that boundary voltage can be measured while conductivity distribution is generally unknown in real cases. The process of reconstructing conductivity distribution from boundary measurement is defined as the inverse problem of EIT 32 . For small change of conductivity distribution Δ σ , the boundary voltage variation Δ U can be simplified and formulated as

normalΔ U = \frac{dU}{dσ} normalΔ σ + O ((), {((), normalΔ σ)}^{2}) \approx \frac{dU}{dσ} normalΔ σ .

Based on finite element method, Equation () is discretized and reformulated as

r = italicSg,

where

r \in ℝ^{m \times 1}

is the discretized difference voltage,

S \in ℝ^{m \times n}

stands for sensitivity matrix, which can be obtained by solving forward problem according to Geselowitz's sensitivity theorem, 33

g \in ℝ^{n \times 1}

is the discretized difference conductivity, m denotes the number of boundary measurements and n is the pixels number inside Ω.…”

Section: Modeling Of Eitmentioning

confidence: 99%

Reconstruction of conductivity distribution with a compound variational strategy in electrical impedance tomography

Shi

Tian

Wang

et al. 2021

Int J Imaging Syst Tech

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As a potential imaging technique, electrical impedance tomography (EIT) is advantageous for reconstructing conductivity distribution. However, due to insufficient measurement, visualization is inevitably an ill-posed inverse problem. Moreover, reconstruction quality is affected by noise. To address these challenges, a novel variational model with an L p -norm as fidelity and a hybrid total variation as penalty (L p -HTV) is proposed for conductivity distribution reconstruction. Iterative reweighted L 1 algorithm transforms the L p -norm to an L 1 -norm and alternating direction method of multipliers is then utilized to solve objective function. Meanwhile, region of interest is defined to enhance robustness to noise and reduce staircase artifact. The performance of the proposed compound method is validated by several cases. Phantom experiments are also conducted. Compared with classic regularization methods, it is found that images reconstructed by the proposed method show large improvement. The results demonstrate that the proposed strategy is more competitive in visualizing conductivity distribution.

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A data compensation method for reducing impact of scalp dehydration in electrical impedance tomography

Shi¹,

Li²,

Wang³

et al. 2022

Sensors and Actuators A: Physical

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Depth Analysis of Planar Array for 3D Electrical Impedance Tomography

Cited by 12 publications

References 21 publications

Super-Resolution Electrochemical Impedance Imaging with a 100 × 100 CMOS Sensor Array

Super-Resolution Electrochemical Impedance Imaging with a 100 × 100 CMOS Sensor Array

Reconstruction of conductivity distribution with a compound variational strategy in electrical impedance tomography

A data compensation method for reducing impact of scalp dehydration in electrical impedance tomography

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